Using daily return data from the four major Central and Eastern European stock markets including fourteen highly liquid stocks and ATX (Vienna), PX (Prague), BUX (Budapest), and WIG20 (Warsaw) market indices, we model the value-at-risk using a set of univariate GARCH-type models. Our results show that, in both in-sample and out-of-sample value-at-risk estimations, the models based on asymmetric distribution of the error term tend to perform better or at least as well as the models based on symmetric distribution (i.e., Normal or Student) when the left tails of daily return distributions are concerned. Evaluation of the same models is less clear, however, when the right tails of the distribution of daily returns must be modelled. We suggest an asset-specific approach to selecting the correct parametric VaR model that depends not only on the risk level considered but also on the position in the underlying asset.